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Open AccessArticle

Detecting Stems in Dense and Homogeneous Forest Using Single-Scan TLS

Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Beijing 100094, China
University of Chinese Academy of Sciences, Beijing 100039, China
Department of Geography, University of North Texas, Denton, TX 76203-5017, USA
Faculty of Forestry, University of Toronto, ON M5S 2E8, Canada
Beijing Zoo, Xizhimenwai Street, Xicheng District, Beijing100044, China
Author to whom correspondence should be addressed.
Academic Editors: Juha Hyyppä, Xinlian Liang and Eetu Puttonen
Forests 2015, 6(11), 3923-3945;
Received: 31 August 2015 / Revised: 31 August 2015 / Accepted: 28 October 2015 / Published: 30 October 2015
(This article belongs to the Special Issue Forest Ground Observations through Terrestrial Point Clouds)
Stem characteristics of plants are of great importance to both ecology study and forest management. Terrestrial laser scanning (TLS) may provide an effective way to characterize the fine-scale structures of vegetation. However, clumping plants, dense foliage and thin structure could intensify the shadowing effect and pose a series of problems in identifying stems, distinguishing neighboring stems, and merging disconnected stem parts in point clouds. This paper presents a new method to automatically detect stems in dense and homogeneous forest using single-scan TLS data. Stem points are first identified with a two-scale classification method. Then a clustering approach is used to group the candidate stem points. Finally, a direction-growing algorithm based on a simple stem curve model is applied to merge stem points. Field experiments were carried out in two different bamboo plots with a stem density of about 7500 stems/ha. Overall accuracy of the stem detection is 88% and the quality of detected stems is mainly affected by the shadowing effect. Results indicate that the proposed method is feasible and effective in detection of bamboo stems using TLS data, and can be applied to other species of single-stem plants in dense forests. View Full-Text
Keywords: single-scan TLS; dense forest; two-scale classification; stem mapping single-scan TLS; dense forest; two-scale classification; stem mapping
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MDPI and ACS Style

Xia, S.; Wang, C.; Pan, F.; Xi, X.; Zeng, H.; Liu, H. Detecting Stems in Dense and Homogeneous Forest Using Single-Scan TLS. Forests 2015, 6, 3923-3945.

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